Probabilistic Population Codes

MODELING EARLY VISION: PROBABILISTIC COMPUTATION USING SPIKING NEURONS, POPULATION CODES, AND CUDA by DANIEL

Probabilistic Assessment of Earthquake Damage and Loss for the City of Tehran, Iran

Tehran is one of the densely populated metropolises located in earthquake-prone regions. Tehran, the population of which surpasses 8 million people, is the most populated area in Iran. There are historical evidences confirming that catastrophic earthquakes have destroyed the city in past years. In the present paper, our study covers all parts of Tehran because there is the potential of signific...

Spiking networks for Bayesian inference and choice.

Systems neuroscience traditionally conceptualizes a population of spiking neurons as merely encoding the value of a stimulus. Yet, psychophysics has revealed that people take into account stimulus uncertainty when performing sensory or motor computations and do so in a nearly Bayes-optimal way. This suggests that neural populations do not encode just a single value but an entire probability dis...

Probabilistic Interpretation of Population Codes

We present a general encoding-decoding framework for interpreting the activity of a population of units. A standard population code interpretation method, the Poisson model, starts from a description as to how a single value of an underlying quantity can generate the activities of each unit in the population. In casting it in the encoding-decoding framework, we find that this model is too restr...

Inference by Reparameterization in Neural Population Codes

Behavioral experiments on humans and animals suggest that the brain performs probabilistic inference to interpret its environment. Here we present a new generalpurpose, biologically-plausible neural implementation of approximate inference. The neural network represents uncertainty using Probabilistic Population Codes (PPCs), which are distributed neural representations that naturally encode pro...

Combining Probabilistic Population Codes

We study the problem of statistically correct inference in networks whose basic representations are population codes. Population codes are ubiquitous in the brain, and involve the simultaneous act ivi ty of many units coding for some low dimensional quantity. A classic example are place cells in the rat hippocampus: these fire when the animal is at a particular place in an environment, so the u...